Forecasting US presidential elections: New approaches (an introduction)

Abstract

We know that candidates and campaigns matter in democratic elections, but that knowledge may not be readily observed in most structural models of national election forecasting. For one, these models virtually never include direct, explicit candidate-related campaign variables as predictors. At most, these candidate/campaign variables are picked up indirectly, usually in polling measures, such as vote intention. For another, the models often manage accurate, ex ante forecasts of US presidential election results, even without the obvious pr​esence of such variables. In this effort, we aim to overcome this paradox by including more direct candidate and campaign measures in a long-standing structural equation model of presidential election forecasting, namely the Political Economy model. We find that inclusions of these candidate and campaign variables do improve the theoretical specification and the statistical performance of the model, and do yield generally more accurate forecasts. However, at least for the test case of the 2016 contest, that increased precision failed to substantively alter the Clinton popular vote forecast.